2023

1. •Ngu, N. H., Thanh, N.N. (Corresponding author), Duc, T.T., Non, D.Q., An, N.T.T., & Chotpantarat, S. Active learning-based random forest algorithm used for soil texture classification mapping in Central Vietnam. Catena, 234, 107629. [Q1,  IF. 6.2]

2. •Thanh, N. N., & Chotpantarat, S. (2023). Geographic Information System and Remote Sensing in Deciphering Groundwater Potential Zones. In Emerging Technologies for Water Supply, Conservation and Management (pp. 133-169). Cham: Springer International Publishing. [Book chapter]

3. •Sumdang, N., Chotpantarat, S., Cho, K. H., & Thanh, N. N. (2023). The risk assessment of arsenic contamination in the urbanized coastal aquifer of Rayong groundwater basin, Thailand using the machine learning approach. Ecotoxicology and Environmental Safety, 253, 114665. [Q1; IF. 7.1]

•Thanh, N. N., Chotpantarat, S., Ha, N. T., & Trung, N. H. (2023). Determination of conditioning factors for mapping nickel contamination susceptibility in groundwater in Kanchanaburi Province, Thailand, using random forest and maximum entropy. Environmental Geochemistry and Health, 1-20. [Q1, IF. 4.8]

2022

•Thanh, N. N., Chotpantarat, S., Trung, N. H., & Ngu, N. H. (2022). Mapping groundwater potential zones in Kanchanaburi Province, Thailand by integrating of analytic hierarchy process, frequency ratio, and random forest. Ecological Indicators, 145, 109591. [Q1, IF. 6.2]

•Thanh, N. N., Thunyawatcharakul, P., Ngu, N. H., & Chotpantarat, S. (2022). Global review of groundwater potential models in the last decade: Parameters, model techniques, and validation. Journal of Hydrology, 128501. [Q1, IF 6.7]